Nuclear parton distribution functions with uncertainties in a general mass variable flavor number scheme

TL;DR

This paper presents a new set of nuclear parton distribution functions at NLO and NNLO accuracy, incorporating diverse experimental data and advanced theoretical corrections, with uncertainties quantified via the Hessian method.

Contribution

It introduces a comprehensive nuclear PDF set using an extended dataset and improved theoretical schemes, including target mass and higher twist effects, within a modified APFEL framework.

Findings

The new nuclear PDFs fit experimental data well.

Uncertainties are systematically quantified.

The results agree with existing nuclear PDF sets.

Abstract

In this article we obtain a new set of nuclear parton distribution functions (nuclear PDFs) at next-to-leading order and next-to-next-to-leading order accuracy in perturbative QCD. The common nuclear deep-inelastic scattering (DIS) data analyzed in our study are complemented by the available charged-current neutrino DIS data with nuclear targets and data from Drell-Yan cross-section measurements for several nuclear targets. In addition, the most recent DIS data from the Jefferson Lab CLAS and Hall C experiments are also added to our data sample. For these specific datasets, we consider the impact of target mass corrections and higher twist effects which are expected to be important in the region of large $x$ and intermediate-to-low $Q^2$. Our analysis is based on a publicly available open-source tool {\tt APFEL}, which has been modified to be applicable for our analysis of nuclear PDFs. Heavy quark contributions to nuclear DIS are considered within the framework of the {\tt FONLL} general-mass variable-flavor-number scheme. The most recent {\tt CT18} PDFs are used as baseline proton PDFs. The uncertainties of nuclear PDFs are determined using the standard Hessian approach. The results of our global QCD analysis are compared with existing nuclear PDF sets and with the fitted cross-sections, for which our set of nuclear PDFs provides a very good description.